This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # Tue Jun 7 19:35:18 2022 ——————————
source("tianfengRwrappers.R")
rat Frontier GSE174098 carotid
rat10x <- CreateSeuratObject(Read10X("./rat_scRNAseq/"), names.field = 2, names.delim = "-",
project = "rat", min.cells = 10, min.features = 300) %>%
PercentageFeatureSet(pattern = "^Mt-", col.name = "percent.mt")
table(rat10x$orig.ident)
VlnPlot(rat10x,"nCount_RNA") /
VlnPlot(rat10x,"percent.mt") /
VlnPlot(rat10x, "nFeature_RNA")
rat10x <- rat10x %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
nCount_RNA > 1000 & nCount_RNA < 30000 & percent.mt< 10) %>%
SCTransform(vars.to.regress = "percent.mt", verbose = F) %>%
RunPCA() %>% FindNeighbors(dims = 1:20) %>%
RunUMAP(dims = 1:20) %>%
FindClusters(resolution = 0.1)

mouse coronary GSE131778
mouse_coronary_countmatrix <- read.csv("./GSE131776_mouse_scRNAseq.txt", sep = "\t")
func <- function(s) {
paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(mouse_coronary_countmatrix) <- lapply(colnames(mouse_coronary_countmatrix), func) # 拆分样本
mousecor <- CreateSeuratObject(counts = mouse_coronary_countmatrix,
project = "mouse_cor", min.cells = 10, min.features = 300) %>% PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt")
# saveRDS(mousecor,"mousecor.rds")
table(mousecor$orig.ident)
VlnPlot(mousecor,"nCount_RNA") /
VlnPlot(mousecor,"percent.mt") /
VlnPlot(mousecor, "nFeature_RNA")
mousecor <- mousecor %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
nCount_RNA > 1000 & nCount_RNA < 30000 & percent.mt < 10) %>%
SCTransform(vars.to.regress = "percent.mt", verbose = F) %>%
RunPCA() %>% FindNeighbors(dims = 1:20) %>%
RunUMAP(dims = 1:20) %>%
FindClusters(resolution = 0.1)
table(mousecor$orig.ident)
saveRDS(mousecor,"mousecor.rds")
SMC2
mousecor <- readRDS("mousecor.rds")
f("Prdm16",mousecor, label.size = 7) + theme(legend.text = element_text(size = 20))
umapplot(mousecor_stromal, label.size = 6) + theme(legend.text = element_text(size = 20))
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

mouse carotid scRNAseq GSE155513
dataload
count_mats <- list.files("./GSE155513_RAW/")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(file) {
dd <- read.table(paste0("./GSE155513_RAW/", file), row.names = 1,stringsAsFactors = F)
colnames(dd) <- as.character(dd['gene',])
dd <- dd[-1,]
CreateSeuratObject(
counts = dd,
project = file, min.cells = 10, min.features = 300
)
})
# 合并seurat对象
mouse_carotid <- merge(allList[[1]],
y = allList[-1], add.cell.ids = count_mats,
project = "mouse_carotid"
)
rm(allList)
# saveRDS(mouse_carotid,"mouse_carotid.rds")
# View(mouse_carotid)
process
mouse_carotid <- readRDS("mouse_carotid.rds")
mouse_carotid <- mouse_carotid %>% PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") %>%
subset(subset = nFeature_RNA > 500 & nFeature_RNA < 3000 &
nCount_RNA > 1000 & nCount_RNA < 20000 & percent.mt < 10) %>%
SCTransform(vars.to.regress = "percent.mt", verbose = F) %>%
RunPCA() %>% FindNeighbors(dims = 1:20) %>%
RunUMAP(dims = 1:20) %>%
FindClusters(resolution = 0.1)
saveRDS(mouse_carotid,"mouse_carotid.rds")
SMC

stromal cells
mouse_carotid_stromal <- subset(mouse_carotid,idents = c(0,2,1,7))
mouse_carotid_stromal <- mouse_carotid_stromal %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>%
RunUMAP(dims = 1:20) %>% FindClusters(resolution = 0.1)
mouse_carotid_stromal <- mouse_carotid_stromal %>% FindClusters(resolution = 0.2)
mouse_carotid_stromal <- readRDS("mouse_carotid_stromal.rds")
# saveRDS(mouse_carotid_stromal,"mouse_carotid_stromal.rds")
umapplot(mouse_carotid_stromal)
f("Dlx2",mouse_carotid_stromal) #Dlx2,Dlx5,Dlx6共同定位
分群表
table(group_tab[Dlx5poscells])/table(group_tab)
GSM4705592_RPS003_matrix.txt.gz GSM4705593_RPS004_matrix.txt.gz GSM4705594_RPS011_matrix.txt.gz GSM4705595_RPS012_matrix.txt.gz GSM4705596_RPS007_matrix.txt.gz
0.080645161 0.076949502 0.130000000 0.029882604 0.058282209
GSM4705597_RPS008_matrix.txt.gz GSM4705598_RPS001_matrix.txt.gz GSM4705599_RPS002_matrix.txt.gz GSM4705600_RPS017_matrix.txt.gz GSM4705601_RPS018_matrix.txt.gz
0.004746835 0.039230575 0.003976143 0.093492209 0.095634096
GSM4705602_RPS013_matrix.txt.gz GSM4705603_RPS014_matrix.txt.gz GSM4705604_RPS015_matrix.txt.gz GSM4705605_RPS016_matrix.txt.gz
0.066773504 0.019255456 0.042503503 0.030303030
图

human ds2
p <- multi_featureplot(c("FRZB","SOST","DLX5","DLX6"), ds2, labels = NA, label.size = 6)
ggsave("refds2_SMC2_carotid_stromal.png",plot = p, height = 7, width = 7,device = png)
Warning: Removed 1 rows containing missing values (geom_text).
human bulk RNA-seq GSE120521 carotid stable/unstable FPKM
fpkm2tpm <- function(fpkm){
exp(log(fpkm) - log(sum(fpkm)) + log(1e6))
}
fpkm_matrix <- read.csv("GSE120521_FPKM.csv")
# fpkm_matrix <- distinct(fpkm_matrix) #去除重复行
fpkm_matrix <- fpkm_matrix[!duplicated(fpkm_matrix$name),]
rownames(fpkm_matrix) <- fpkm_matrix$name
fpkm_matrix$name <- NULL
tpm_matrix <- apply(fpkm_matrix, 2, fpkm2tpm)
colSums(tpm_matrix)
group_file <- c("stable","unstable","stable","unstable",
"stable","unstable","stable","unstable")
boxplot(tpm_matrix, las = 2)
expr_mat <- tpm_matrix[!apply(tpm_matrix, 1, function(x){sum(floor(x) == 0)>3}),]
boxplot(expr_mat, las = 2)
library(limma)
expr_mat <- normalizeBetweenArrays(expr_mat)
expr_mat <- log2(expr_mat+1) #使用log2 scale
#PCA
library(ggfortify)
df <- as.data.frame(t(expr_mat))
df$group <- group_file
autoplot(prcomp(df[,1:(ncol(df)-1)]), data=df, colour = 'group')+ theme_bw()
# If the sequencing depth is reasonably consistent across the RNA samples, then the simplest and most robust approach to differential exis to use limma-trend.
fit <- lmFit(expr_mat, group_file)
fit <- treat(fit, lfc=log2(1.2), trend=TRUE)
topTreat(fit, coef=ncol(design))
library(ggpubr)
dat <- expr_mat
design <- model.matrix(~factor(group_file))
fit <- lmFit(dat, design)
fit <- eBayes(fit)
# options(digits = 4)
topTable(fit,coef=2,adjust='BH')
deg <- topTable(fit,coef=2,adjust='BH',number = Inf)
head(deg)
write.csv(deg,"./datatable/stable vs unstable.csv")
## look up FRZB, SOST, PRDM6, OGN
ds1markers[ds1markers$cluster == "SMC2",]$gene
ds2markers[ds2markers$cluster == "SMC3",]$gene
smc2markers <- intersect(ds1markers[ds1markers$cluster == "SMC2",]$gene, ds2markers[ds2markers$cluster == "SMC3",]$gene)
deg[intersect(smc2markers, rownames(deg)),]
ds1markers[ds1markers$cluster == "SMC1",]$gene
ds2markers[ds2markers$cluster == "SMC1",]$gene
SMC1markers <- intersect(ds1markers[ds1markers$cluster == "SMC1",]$gene, ds2markers[ds2markers$cluster == "SMC1",]$gene)
deg[intersect(SMC1markers, rownames(deg)),]

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---
title: "R Notebook"
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---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*. 
# Tue Jun  7 19:35:18 2022 ------------------------------


```{r}
source("tianfengRwrappers.R")
```

## rat Frontier *GSE174098* carotid
```{r fig.width=4, fig.height=8}
rat10x <- CreateSeuratObject(Read10X("./rat_scRNAseq/"), names.field = 2, names.delim = "-",
                                   project = "rat", min.cells = 10, min.features = 300) %>%
  PercentageFeatureSet(pattern = "^Mt-", col.name = "percent.mt") 

table(rat10x$orig.ident)
VlnPlot(rat10x,"nCount_RNA") /
VlnPlot(rat10x,"percent.mt") /
VlnPlot(rat10x, "nFeature_RNA")


rat10x <- rat10x %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
                              nCount_RNA > 1000 &  nCount_RNA < 30000 & percent.mt< 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

```


```{r fig.width=4, fig.height=3}
umapplot(rat10x)
multi_featureplot(c("Acta2","Cnn1","Fn1","Lum","Vcam1","Ly6a","Dlx5","Dlx6","Sost"), rat10x)


f("Bmpr1b", rat10x) /
f("Bmpr1a", rat10x)
table(rat10x$seurat_clusters)

rat10x0 <- subset(rat10x,ident = 0)

f("Sost",rat10x)
ncol(subset(rat10x0, subset = Bmpr1a > 0))
ncol(subset(rat10x0, subset = Bmpr1b > 0))
ncol(subset(rat10x0, subset = Bmpr1a > 0 & Bmpr1b > 0))

ncol(subset(rat10x0, subset = Sost > 0 & Bmpr1b > 0))
ncol(subset(rat10x0, subset = Sost > 0 & Bmpr1a > 0))

ncol(rat10x0)
```


# mouse coronary *GSE131778*
```{r}
mouse_coronary_countmatrix <- read.csv("./GSE131776_mouse_scRNAseq.txt", sep = "\t")
func <- function(s) {
  paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(mouse_coronary_countmatrix) <- lapply(colnames(mouse_coronary_countmatrix), func) # 拆分样本
```

```{r fig.width= 4, fig.height=8}
mousecor <- CreateSeuratObject(counts = mouse_coronary_countmatrix,
                                   project = "mouse_cor", min.cells = 10, min.features = 300) %>%  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") 

# saveRDS(mousecor,"mousecor.rds")
table(mousecor$orig.ident)
VlnPlot(mousecor,"nCount_RNA") /
VlnPlot(mousecor,"percent.mt") /
VlnPlot(mousecor, "nFeature_RNA")


mousecor <- mousecor %>% subset(subset = nFeature_RNA > 400 & nFeature_RNA < 4000 &
                              nCount_RNA > 1000 &  nCount_RNA < 30000 & percent.mt < 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)
table(mousecor$orig.ident)
saveRDS(mousecor,"mousecor.rds")
```

### SMC2
```{r fig.width=4, fig.height=3}
mousecor <- readRDS("mousecor.rds")
f("Prdm16",mousecor, label.size = 7) + theme(legend.text = element_text(size = 20))
```


```{r fig.width=4, fig.height=3}
mousecor_stromal <- subset(mousecor,idents = c(0,1,2))

mousecor_stromal <- mousecor_stromal %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% FindClusters(resolution = 0.2)
mousecor_stromal <- mousecor_stromal %>% FindClusters(resolution = 0.2)


mousecor_stromal <- readRDS("mousecor_stromal.rds")

umapplot(mousecor_stromal, label.size = 6) + theme(legend.text = element_text(size = 20))
multi_featureplot(c("Acta2","Cnn1","Fn1","Lum","Vcam1","Ly6a","Dlx5","Dlx6"), mousecor_stromal,labels = NA)


levels(Idents(mousecor_stromal)) <- c("SMC1","Fibroblast1","Fibromyocyte","SMC1","Fibroblast2","SMC2")
f("Acta2",mousecor_stromal,label.size = 6)
p <- multi_featureplot(c("Acta2","Cnn1","Fn1","Lum","Vcam1","Ly6a","Dlx5","Dlx6","Lgals3"),mousecor_stromal,labels = NA,label.size = 6)
ggsave("natmed_mouse_coronary_stromal2.png",plot = p, height = 12, width = 16,device = png)
umapplot(mousecor_stromal, label.size = 6) + theme(legend.text = element_text(size = 18))
```

# mouse carotid scRNAseq *GSE155513*
### dataload
```{r}
count_mats <- list.files("./GSE155513_RAW/")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(file) {
  dd <- read.table(paste0("./GSE155513_RAW/", file), row.names = 1,stringsAsFactors = F)
  colnames(dd) <- as.character(dd['gene',])
  dd <- dd[-1,]
  CreateSeuratObject(
    counts = dd,
    project = file, min.cells = 10, min.features = 300
  )
})
# 合并seurat对象
mouse_carotid <- merge(allList[[1]], 
  y = allList[-1], add.cell.ids = count_mats,
  project = "mouse_carotid"
)
rm(allList)

# saveRDS(mouse_carotid,"mouse_carotid.rds")
# View(mouse_carotid)
```

### process
```{r}
mouse_carotid <- readRDS("mouse_carotid.rds")

mouse_carotid <- mouse_carotid %>%  PercentageFeatureSet(pattern = "^mt-", col.name = "percent.mt") %>%
subset(subset = nFeature_RNA > 500 & nFeature_RNA < 3000 &
                              nCount_RNA > 1000 &  nCount_RNA < 20000 & percent.mt < 10) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

saveRDS(mouse_carotid,"mouse_carotid.rds")
```

### SMC
```{r}
mouse_carotid <- readRDS("mouse_carotid.rds")
umapplot(mouse_carotid)
umapplot(mouse_carotid,group.by = "orig.ident",label = F)
f("Prdm16",mouse_carotid) #SMC
f("Ly6a",mouse_carotid) #SEM-like cells

multi_featureplot(c("Bmp2","Bmp4","Bmp6"),mouse_carotid)

## BMP4 在这里EC的*大部分*中表达，而在人类样本中BMP4+ EC细胞是少数的
markers <- FindAllMarkers(mouse_carotid,logfc.threshold = 0.5,min.diff.pct = 0.2, only.pos = T)
```
### stromal cells
```{r fig.width=4, fig.height=3}
mouse_carotid_stromal <- subset(mouse_carotid,idents = c(0,2,1,7))
mouse_carotid_stromal <- mouse_carotid_stromal %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% FindClusters(resolution = 0.1)
mouse_carotid_stromal <- mouse_carotid_stromal %>% FindClusters(resolution = 0.2)

mouse_carotid_stromal <- readRDS("mouse_carotid_stromal.rds")
# saveRDS(mouse_carotid_stromal,"mouse_carotid_stromal.rds")

umapplot(mouse_carotid_stromal)
f("Dlx2",mouse_carotid_stromal) #Dlx2,Dlx5,Dlx6共同定位
```

#### 分群表
```{r fig.width=4, fig.height=3}
Dlx5poscells <- WhichCells(mouse_carotid_stromal, expression = `Dlx6` > 0 & `Dlx5` > 0)
group_tab <- Idents(mouse_carotid_stromal)
table(group_tab[Dlx5poscells])/table(group_tab) 
# 在cluster5 有43.8%的细胞表达DLX5，38.4%的细胞表达DLX6,20.2%的细胞表达两者

## 关于样本信息
Dlx5poscells <- WhichCells(mouse_carotid_stromal, idents = "DLX SMC")
group_tab <- mouse_carotid_stromal$orig.ident
table(group_tab[Dlx5poscells])/table(group_tab) 
table(group_tab[Dlx5poscells])
```

#### 图
```{r fig.width=4, fig.height=3}
levels(Idents(mouse_carotid_stromal)) <- c("SEM cell","Fibroblast1","SMC1","SMC2","Fibroblast2","DLX SMC","Unannotated","Unannotated")
umapplot(mouse_carotid_stromal,label.size = 6,label = F) + theme(legend.text = element_blank())

p <- multi_featureplot(c("Acta2","Cnn1","Fn1","Lum","Vcam1","Ly6a","Dlx5","Dlx6","Lgals3"),mouse_carotid_stromal,labels = NA, label.size = 6)
ggsave("cir_mouse_carotid_stromal2.png",plot = p, height = 12, width = 16,device = png)
Dotplot(c("Acta2","Cnn1","Fn1","Lum","Vcam1","Ly6a","Dlx5","Dlx6","Sost"),mouse_carotid_stromal)

DLXposSMC_markers <- FindMarkers(mouse_carotid_stromal, ident.1 = "DLX SMC", logfc.threshold = 0.4, only.pos = T, min.diff.pct = 0.2)
f("Dlx5",mouse_carotid_stromal)

library(org.Mm.eg.db)
GO_dotplot(rownames(DLXposSMC_markers), OrgDb = org.Mm.eg.db)
```
# human ds2
```{r}
p <- multi_featureplot(c("ACTA2","CNN1","FN1","LUM","VCAM1","LY6A","DLX5","DLX6","LGALS3","SOST"), ds2, labels = NA, label.size = 6)

ggsave("refds2_carotid_stromal.png",plot = p, height = 12, width = 12,device = png)


p <- multi_featureplot(c("FRZB","SOST","DLX5","DLX6"), ds2, labels = NA, label.size = 6)
ggsave("refds2_SMC2_carotid_stromal.png",plot = p, height = 7, width = 7,device = png)
```


# human bulk RNA-seq *GSE120521* carotid stable/unstable FPKM
```{r}
fpkm2tpm <- function(fpkm){
  exp(log(fpkm) - log(sum(fpkm)) + log(1e6))
}

fpkm_matrix <- read.csv("GSE120521_FPKM.csv")
# fpkm_matrix <- distinct(fpkm_matrix) #去除重复行
fpkm_matrix <- fpkm_matrix[!duplicated(fpkm_matrix$name),]
rownames(fpkm_matrix) <- fpkm_matrix$name
fpkm_matrix$name <- NULL

tpm_matrix <- apply(fpkm_matrix, 2, fpkm2tpm)
colSums(tpm_matrix)

group_file <- c("stable","unstable","stable","unstable",
                "stable","unstable","stable","unstable")
boxplot(tpm_matrix, las = 2)

expr_mat <- tpm_matrix[!apply(tpm_matrix, 1, function(x){sum(floor(x) == 0)>3}),]

boxplot(expr_mat, las = 2)

library(limma)
expr_mat <- normalizeBetweenArrays(expr_mat)
expr_mat <- log2(expr_mat+1) #使用log2 scale

#PCA
library(ggfortify) 
df <- as.data.frame(t(expr_mat)) 
df$group <- group_file 
autoplot(prcomp(df[,1:(ncol(df)-1)]), data=df, colour = 'group')+ theme_bw() 

# If the sequencing depth is reasonably consistent across the RNA samples, then the simplest and most robust approach to differential exis to use limma-trend.

fit <- lmFit(expr_mat, group_file)
fit <- treat(fit, lfc=log2(1.2), trend=TRUE)
topTreat(fit, coef=ncol(design))

```


```{r}
library(ggpubr)
dat <- expr_mat
design <- model.matrix(~factor(group_file))
fit <- lmFit(dat, design)
fit <- eBayes(fit)
# options(digits = 4)
topTable(fit,coef=2,adjust='BH')
deg <- topTable(fit,coef=2,adjust='BH',number = Inf)
head(deg) 

write.csv(deg,"./datatable/stable vs unstable.csv")
## look up FRZB, SOST, PRDM6, OGN
ds1markers[ds1markers$cluster == "SMC2",]$gene

ds2markers[ds2markers$cluster == "SMC3",]$gene

smc2markers <- intersect(ds1markers[ds1markers$cluster == "SMC2",]$gene, ds2markers[ds2markers$cluster == "SMC3",]$gene)


deg[intersect(smc2markers, rownames(deg)),]

ds1markers[ds1markers$cluster == "SMC1",]$gene

ds2markers[ds2markers$cluster == "SMC1",]$gene

SMC1markers <- intersect(ds1markers[ds1markers$cluster == "SMC1",]$gene, ds2markers[ds2markers$cluster == "SMC1",]$gene)

deg[intersect(SMC1markers, rownames(deg)),]
```

```{r}
# logFC_threshold = 1
# pvalue_threshold = 0.05
selected_genes = as.character(read.table("SMC2")$V1)

volcano_plot <- function(filename, selected_genes, logFC_threshold = 1, pvalue_threshold = 0.05)
{
  f<-read.csv("./datatable/stable vs unstable.csv")
  f$threshold <- factor(ifelse(f$adj.P.Val < pvalue_threshold & abs(f$logFC) >= logFC_threshold, 
                              ifelse(f$logFC>= logFC_threshold ,'Up','Down'),'N.S.'),
                       levels=c('Up','Down','N.S.'))
  
   ggplot(f,aes(x=logFC,y=-log10(adj.P.Val),color=threshold))+
    geom_point()+
    scale_color_manual(values=c("#CC0000","#2f5688","#BBBBBB"))+
    geom_text_repel(
      data = f[f$X %in% selected_genes,],
      aes(label = X),
      size = 5, max.overlaps = 1000,
      col="black", segment.color = "black", show.legend = FALSE )+
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
          panel.background = element_blank(), axis.line = element_line(colour = "black"))+
    theme(
      axis.title.x = element_text(size = 20), axis.text.x = element_text(size = 15),
      axis.title.y = element_text(size = 20), axis.text.y = element_text(size = 15), 
      legend.text = element_text(size = 20), 
      legend.title = element_blank()
    )+
    ylab('-log10 (p-adj)') +
    xlab('log2 (FoldChange)') +
    geom_vline(xintercept=c(-logFC_threshold,logFC_threshold),lty=3,col="black",lwd=0.5) +
    geom_hline(yintercept =c(0,-log10(pvalue_threshold)),lty=3,col="black",lwd=0.5)
}

selected_genes <- c(as.character(read.table("SMC2")$V1))

p <- volcano_plot("./datatable/stable vs unstable.csv",selected_genes) + ggtitle("stable vs unstable, SMC2 marker")

ggsave("SMC2_stable vs unstable.png",plot = p,device = png,height = 4, width = 6)
```


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